2022/07/06 by Mathias Lindholm, Ronald Richman, Lindholm, Mathias +5
Computer Science · #68T07 #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #FOS: Economics and business #Finance #Machine Learning (cs.LG) #Machine Learning in Healthcare #Statistical Finance (q-fin.ST) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2207.02799
openalex publication_date 2022/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models, and are thus having an undesirable (or illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such approaches require full knowledge of policyholders' protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics, and it produces prices that are free from proxy discrimination. We demonstrate the use of the proposed model and we find that its predictive accuracy is comparable to a conventional feedforward neural network (on full information). However, this multi-task network has clearly superior performance in the case of partially missing policyholder information.